\section{Introduction}
\label{sec:intro}
%Mobile crowdsensing (MCS) has gained popularity
%in recent years becoming an appealing paradigm for
%sensing and collecting data.
With the prevalence of portable devices,
Mobile CrowdSensing (MCS) has recently
become a promising paradigm for
various sensing tasks~\cite{capponi2019survey},
such as the monitoring of
air quality~\cite{pan2017crowdsensing},
traffic~\cite{liu2017participatory},
and urban infrastructure~\cite{aly2016automatic}.
Exploiting MCS to conduct object tracking
has been attracting increasing research attentions
\cite{chen2020tagray,sun2015securefind,
chen2019crowdtracking}.
Different from traditional object tracking approaches mainly relied on
the stationary cameras pre-deployed in the urban area
\cite{wu2017node},
MCS-based tracking systems outsource tracking tasks
to the participants in the real world.
%Traditional object tracking approaches mainly rely on
%the stationary cameras pre-deployed in the urban area
%\cite{wu2017node,liu2009dynamic},
%which bring challenges simultaneously, e.g.,
%how to determine the proper density of deployed cameras
%to achieve the tradeoff
%between the cost and the size of the tracking region,
%and how to identify the same moving target precisely
%though various camera cooperations~\cite{wang2019spm,wu2020future}.
%Adopting MCS to track the object moving can effectively
%remedy the limitations of conventional camera schemes,
%e.g., enhances spatio-temporal coverage in the non-camera
%area by the mobile ability of crowdsensing.
%expand the tracking area and reduce the tracking time.
A real-life example of MCS-based tracking system is deployed
on Alipay APP in China, which encourages the users to participate
the missing children finding
%(the information is released on the APP)
as soon as possible.

Many early researches on MCS-based object tracking
usually require special devices~\cite{guo2018task},
e.g., low-power BLE peripheral~\cite{liu2014finding}
and unique Bluetooth tags~\cite{sun2015securefind}
to conduct tracking tasks.
With the rapid development of mobile networks
and the wide usage of sensor-enhanced smartphones,
using the smartphone cameras to
build crowd tracking systems
has become a popular paradigm to
track object.
The limitations of conventional stationary camera-based schemes,
i.e., how to determine the proper density of deployed cameras
to achieve the tradeoff
between the cost and the size of the tracking region,
and how to identify the same moving target precisely
though various camera cooperations~\cite{wang2019spm},
can be remedied by the wide distribution of citizens.

Recently crowdsourced dynamic camera networks-based
tracking systems~\cite{JingGWLLY18,chen2019crowdtracking},
which employ users to instantaneously take photographs
of the object with the \textit{mobile cameras} of smartphones,
are proposed to track moving objects in their
possible arrival geographical regions,
according to the mobility prediction methods.
With the wide distribution of citizens
and the superior cognition ability of human,
the tracking coverage expanded and cost decreased.

However,
existing MCS-based tracking systems may suffer from
two issues: 1) the predicted arrival region is large so
plenty of users should be recruited to guarantee
tracking coverage, means a high cost;
2) users select their tracking locations randomly
due to the non-cooperative behaviors among users,
leads to a low benefit.
Thus, the critical issues
for object tracking are to
scale down the tracking task by limiting the tracking region
and determine an optimal assignment to
maximize the system utility from a global perspective.

Targeting on the issues mentioned above,
we propose a  Minimum tracking Region
and Maximun system Utility (MRMU) algorithm
to develop MobiTrack, a MCS-based system
that recruits an optimal set of workers to
collaboratively take photographs to
track the object in the city.
%that decomposes object tracking to two stage.
%In the first stage,
%MobiTrack learns from crowdsoursed historical trajectories.
%In the second stage,
%it predicts the future movement of the object
%and generates an optimal assignment
%to maximize the system utility.
The main contributions of this paper are as follows.

\begin{itemize}
    \item We propose a variant of
    the k-means clustering algorithm
    to learn significant places as sensing locations
    from GPS data.
    A $N$-Gram model is constructed
    for object trajectory prediction
    and minimal sensing location number determination.
    \item We formulate the object tracking problem as
    an optimal task assignment problem
    through a novel system utility,
    %which is defined as the tradeoff
%    between the tracking benefit and cost,
    The Maximum Utility Task Assignment (MUTA) algorithm
    is conducted
    to solve it in polynomial time,
    aims to achieve the maximal system utility.
    \item We conduct extensive experiments on
    real-world taxi trajectory datasets to
    evaluate the performance of MobiTrack.
    The results demonstrate that
    the proposed algorithm outperforms
    the other existing algorithms
    for movement prediction and system utility achievement.
\end{itemize}

The rest of this paper is organized as follows. Section~\ref{sec:related} discusses the related work.
The system model and
problem statement are presented in Section~\ref{sec:model}. Section~\ref{sec:method} introduces the proposed solution to
vehicle movement prediction and tracking task assignment.
The datasets and experiment results are presented in Section~\ref{sec:eval} and ~\ref{sec:pe}.
Finally, we conclude the paper in Section~\ref{sec:conclude}.
